This thesis is concerned with the design of press tool run-ofts. Run-off is the area on the punch surrounding the panel and its shape is of great importance to the control of metal flow during the draw. The design, although influenced by engineering considerations, is principally a geometric problem, which traditionally has been time-consuming. The overall objective is to reduce the run-oft definition time and hence improve the lead time. Current Austin Rover design procedures are described in Chapter 2 and form the basis of the C.A.D. program outlined in Chapter 3. This specification distils the need for a number of geometric algorithms. In general, obtaining the required continuity between the panel and run-off surface will require some degree of boundary curve approximation. Chapter 4 details four alternative approximation techniques which are compared in Chapter 5; and constitute the main results of the thesis. The salient issues of run-off surface interpolation are considered in Chapter 6.

No description available.

Chemical process industries are constantly challenged to operate profitably and efficiently, despite the presence of significant uncertainties and disturbances on the operational conditions, and various operational limitations. The capability to meet the challenge relies on the quality of process control design, which should integrate the dynamic controllability characteristics in addition to the traditional economic considerations. The focus of this thesis is the development of a systematic controllability assessment framework for process control design. The framework addresses the controllability aspects in process and controller structures, as well as in time-domain dynamic performances. The aim is to provide clearer relationships between process profitability, controllability, and operational switching strategies in response to variations in the operating conditions. The skeleton of the framework is a mathematical optimisation algorithm. This algorithm considers the structural, operational and economic problems arising in process control design as a progressive, dynamic, and uncertain semi-infinite mixed integer nonlinear programming problem. The algorithm is an iterative, two-level optimisation, which determines the optimum process design and the associated controllability index within an optimisation window. The window progresses along a time horizon, ensuring optimal process design within the window while accommodating the design switching during the course of load variations in a larger time horizon. The controllability index quantifies the design capability to satisfy a given economic objective. Unique to other existing approaches, the process controllability index is computed based on the multi-dimensional geometric representation of the disturbances and uncertainties, measured process dynamics, and feasible operating spaces. These representations account for variable interactions existing in a multivariable process operation, in contrast to separate quantification in traditional single variable assessments. The geometric computation of the index requires the analysis and elimination of redundant measurement variables, which occur in different combinations at different process and controller structures. The redundancy is detected and eliminated based on statistical collinearity among the process data, allowing the assessment to focus on the retained functional variables and the associated critical disturbances and uncertainties. The redundancy analysis is tailored with a dynamic mixed integer nonlinear programming (MINLP) solver, which is dedicated to select the optimum process and controller structure within the design. The solver is developed based on the branch and bound strategy over the design tree, which consists of alternative nonlinear programming (NLP) sub-problems. In addition to the redundancy analysis, the solver is equipped with a compact MINLP formulation, an alternating depth-first and breadth-first search strategy, sub-problems. The tailored strategy ensures fast and efficient convergence of convex problems, as well as superior optimum of non-convex counterparts. Finally, the framework is performed within a time window, which progresses along the time horizon. This strategy provides realistic responses to major variations along greater length of time, by switching between optimum operational modes, while maintaining the optimum process controllability. The performance of the framework is illustrated through several case studies. Each case demonstrates the novelty of addressing various computational features in a concise algorithm. These include the industrial case, which involves the systematic controllability assessment of an industrial five-effect liquor-burning evaporator within an Alumina refinery, which highlights the contribution of this framework in bridging the process design methodologies with the industrial implementation. The thesis consists of eight chapters, presenting the systematic development of the framework. The numerical implementations have been organised in a MATLAB Toolbox, accompanied with the relevant case studies.

Energy consumption is one of the main areas in the study of chemical process design. It is usually referred to as the critical element that is continuously needed for running a chemical process, and is daily effected by the prices of energy. Therefore, poor designs which are not energy integrated normally lead to less profit due to high consumption of energy. These simple economics are the reason for tackling the area of energy integration in process design. A styrene production process is taken to be the model process for carrying out the design work incorporating the various energy integration techniques. A thorough review of the published work in this subject area was the first step in this research work. This has been followed by calculating mass and energy balances around the overall plant and the individual process steps, so that information about flowrates and energy consumed and released was obtained for the base case. After this all the possible distillation sequence configurations were tested in order to find the sequence that required least energy compared with all the other possible sequences. This step is the first part of integrating the distillation train. The second part considered the heat exchanger network associated with the distillation train and this has been taken in the context of overall process integration. "Pinch technology" was used as an aid for targeting the minimum hot and cold utilities required, designing the heat exchanger network that was compatible with the minimum use of utility and to seek further improvements on the process heat exchanger network which made it capable of recovering even more energy. Utility supplies are designed with respect to the process design, hence the next step considered the interaction between the utility and process design. Thus, the utilities were introduced in a more efficient way, resulting in a better heat exchanger network and increasing the interprocess heat exchange. Finally the steam and power system in the styrene plant was tested in order to determine how much this system had benefited due to the overall efficiency of energy supply and demand.

No description available.

Many recent studies on the relative costs and benefits of biofuels have raised the need for a detailed and rigorous analysis of the operations of a biorefinery that is focused on optimization. The current thesis concentrates on the design and optimization of plants for producing biodiesel and ethanol from cellulosic biomass. We have performed numerical simulations combined with systematic parametric analyses to investigate the effect of various parameters on the overall material and energy balances of each biorefinery. The efficiency of the simulated processes was investigated by introducing and/or estimating various metrics in order to select the more beneficial directions for process improvements. Particular emphasis has been paid on heat integration and the design of highly efficient combined heat and power (CHP) units that generate the steam and electricity needed for the purification of biofuels and their co-products. The first part of the thesis is focused on biodiesel production via transesterification of soybean oil with methanol, under alkali-catalyzed conditions. We have analyzed the performance of several reactor configurations in order to improve the conversion of the reversible transesterification reactions. The effect of the oil to alcohol ratio has also been extensively explored. Furthermore, the energy requirements of the simulated process have been rigorously calculated. Since biodiesel facilities can be used either for small-scale, distributed applications or for large-scale production, we have explored whether it is more energy efficient to burn the glycerol-rich stream in a combined heat and power (CHP) plant, or purify the glycerol and use it a feedstock for producing higher-value chemicals with further biotechnological processes. The second part of the thesis focuses on the production of cellulosic ethanol. Having developed the process model, a detailed parametric analysis was carried out to determine how the energy balances and overall efficiency of the biorefinery were influenced by changes in (a) the composition of the biomass feedstock, and (b) the conversion levels of the hydrolysis and fermentation stages. Furthermore, the requirements of the utility section of the ethanol plant were calculated. The utility section included a combined heat and power unit where by-product streams of the production process were utilized for energy generation. The parametric analysis indicated that these streams were in most cases an insufficient fuel source for meeting the energy requirements of the plant and thus, additional fuel was required (biomass, coal, or natural gas). The calculations of this section indicated a significant trade-off between ethanol production and external energy inputs, thus casting some doubt on the ultimate effectiveness of efforts to develop genetically modified energy crops (with high carbohydrate content) in order to maximize fuel production.

This research work presents a new methodology for the simultaneous design and control of chemical processes. One of the most computationally demanding tasks in the integration of process control and process design is the search for worst case scenarios that result in maximal output variability or in process variables being at their constraint limits. The key idea in the current work is to find these worst scenarios by using tools borrowed from robust control theory. To apply these tools, the closed-loop dynamic behaviour of the process to be designed is represented as a robust model. Accordingly, the process is mathematically described by a nominal linear model with uncertain model parameters that vary within identified ranges of values. These robust models, obtained from closed-loop identification, are used in the present method to test the robust stability of the process and to estimate bounds on the worst deviations in process variables in response to external disturbances. The first approach proposed to integrate process design and process control made use of robust tools that are based on the Quadratic Lyapunov Function (QLF). These tests require the identification of an uncertain state space model that is used to evaluate the process asymptotic stability and to estimate a bound (γ) on the random-mean squares (RMS) gain of the model output variability. This last bound is used to assess the worst-case process variability and to evaluate bounds on the deviations in process variables that are to be kept within constraints. Then, these robustness tests are embedded within an optimization problem that seeks for the optimal design and controller tuning parameters that minimize a user-specified cost function. Since the value of γ is a bound on one standard deviation of the model output variability, larger multiples of this value, e.g. 2γ, 3γ, were used to provide more realistic bounds on the worst deviations in process variables. This methodology (γ-based) was applied to the simultaneous design and control of a mixing tank process. Although this approach resulted in conservative designs, it posed a nonlinear constrained optimization problem that required less computational effort than that required by a Dynamic Programming approach which had been the main method previously reported in the literature. While the γ-based robust performance criterion provides a random-mean squares measure of the variability, it does not provide information on the worst possible deviation. In order to search for the worst deviation, the present work proposed a new robust variability measure based on the Structured Singular Value (SSV) analysis, also known as the μ-analysis. The calculation of this measure also returns the critical time-dependent profile in the disturbance that generates the maximum model output error. This robust measure is based on robust finite impulse response (FIR) closed-loop models that are directly identified from simulations of the full nonlinear dynamic model of the process. As in the γ-based approach, the simultaneous design and control of the mixing tank problem was considered using this new μ-based methodology. Comparisons between the γ-based and the μ-based strategies were discussed. Also, the computational time required to assess the worst-case process variability by the proposed μ-based method was compared to that required by a Dynamic Programming approach. Similarly, the expected computational burden required by this new μ-based robust variability measure to estimate the worst-case variability for large-scale processes was assessed. The results show that this new robust variability tool is computationally efficient and it can be potentially implemented to achieve the simultaneous design and control of chemical plants. Finally, the Structured Singular Value-based (μ-based) methodology was used to perform the simultaneous design and control of the Tennessee Eastman (TE) process. Although this chemical process has been widely studied in the Process Systems Engineering (PSE) area, the integration of design and control of this process has not been previously studied. The problem is challenging since it is open-loop unstable and exhibits a highly nonlinear dynamic behaviour. To assess the contributions of different sections of the TE plant to the overall costs, two optimization scenarios were considered. The first scenario considered only the reactor’s section of the TE process whereas the second scenario analyzed the complete TE plant. To study the interactions between design and control in the reactor’s section of the plant, the effect of different parameters on the resulting design and control schemes were analyzed. For this scenario, an alternative calculation of the variability was considered whereby this variability was obtained from numerical simulations of the worst disturbance instead of using the analytical μ-based bound. Comparisons between the analytical bound based strategy and the simulation based strategy were discussed. Additionally, a comparison of the computational effort required by the present solution strategy and that required by a Dynamic Programming based approach was conducted. Subsequently, the topic of parameter uncertainty was investigated. Specifically, uncertainty in the reaction rate coefficient was considered in the analysis of the TE problem. Accordingly, the optimization problem was expanded to account for a set of different values of the reaction rate constant. Due to the complexity associated with the second scenario, the effect of uncertainty in the reaction constant was only studied for the first scenario corresponding to the optimization of the reactor section. The results obtained from this research project show that Dynamic Programming requires a CPU time that is almost two orders of magnitude larger than that required by the methodology proposed here. Likewise, the consideration of uncertainty in a physical parameter within the analysis, such as the reaction rate constant in the Tennessee Eastman problem, was shown to dramatically increase the computational load when compared to the case in which there is no process parametric uncertainty in the analysis. In general, the integration of design and control within the analysis resulted in a plant that is more economically attractive than that specified by solely optimizing the controllers but leaving the design of the different units fixed. This result is particularly relevant for this research work since it justifies the need for conducting simultaneous process design and control of chemical processes. Although the application of the robust tools resulted in conservative designs, the method has been shown to be an efficient computational tool for simultaneous design and control of chemical plants.

This research work presents a new methodology for the simultaneous design and control of chemical processes. One of the most computationally demanding tasks in the integration of process control and process design is the search for worst case scenarios that result in maximal output variability or in process variables being at their constraint limits. The key idea in the current work is to find these worst scenarios by using tools borrowed from robust control theory. To apply these tools, the closed-loop dynamic behaviour of the process to be designed is represented as a robust model. Accordingly, the process is mathematically described by a nominal linear model with uncertain model parameters that vary within identified ranges of values. These robust models, obtained from closed-loop identification, are used in the present method to test the robust stability of the process and to estimate bounds on the worst deviations in process variables in response to external disturbances. The first approach proposed to integrate process design and process control made use of robust tools that are based on the Quadratic Lyapunov Function (QLF). These tests require the identification of an uncertain state space model that is used to evaluate the process asymptotic stability and to estimate a bound (γ) on the random-mean squares (RMS) gain of the model output variability. This last bound is used to assess the worst-case process variability and to evaluate bounds on the deviations in process variables that are to be kept within constraints. Then, these robustness tests are embedded within an optimization problem that seeks for the optimal design and controller tuning parameters that minimize a user-specified cost function. Since the value of γ is a bound on one standard deviation of the model output variability, larger multiples of this value, e.g. 2γ, 3γ, were used to provide more realistic bounds on the worst deviations in process variables. This methodology (γ-based) was applied to the simultaneous design and control of a mixing tank process. Although this approach resulted in conservative designs, it posed a nonlinear constrained optimization problem that required less computational effort than that required by a Dynamic Programming approach which had been the main method previously reported in the literature. While the γ-based robust performance criterion provides a random-mean squares measure of the variability, it does not provide information on the worst possible deviation. In order to search for the worst deviation, the present work proposed a new robust variability measure based on the Structured Singular Value (SSV) analysis, also known as the μ-analysis. The calculation of this measure also returns the critical time-dependent profile in the disturbance that generates the maximum model output error. This robust measure is based on robust finite impulse response (FIR) closed-loop models that are directly identified from simulations of the full nonlinear dynamic model of the process. As in the γ-based approach, the simultaneous design and control of the mixing tank problem was considered using this new μ-based methodology. Comparisons between the γ-based and the μ-based strategies were discussed. Also, the computational time required to assess the worst-case process variability by the proposed μ-based method was compared to that required by a Dynamic Programming approach. Similarly, the expected computational burden required by this new μ-based robust variability measure to estimate the worst-case variability for large-scale processes was assessed. The results show that this new robust variability tool is computationally efficient and it can be potentially implemented to achieve the simultaneous design and control of chemical plants. Finally, the Structured Singular Value-based (μ-based) methodology was used to perform the simultaneous design and control of the Tennessee Eastman (TE) process. Although this chemical process has been widely studied in the Process Systems Engineering (PSE) area, the integration of design and control of this process has not been previously studied. The problem is challenging since it is open-loop unstable and exhibits a highly nonlinear dynamic behaviour. To assess the contributions of different sections of the TE plant to the overall costs, two optimization scenarios were considered. The first scenario considered only the reactor’s section of the TE process whereas the second scenario analyzed the complete TE plant. To study the interactions between design and control in the reactor’s section of the plant, the effect of different parameters on the resulting design and control schemes were analyzed. For this scenario, an alternative calculation of the variability was considered whereby this variability was obtained from numerical simulations of the worst disturbance instead of using the analytical μ-based bound. Comparisons between the analytical bound based strategy and the simulation based strategy were discussed. Additionally, a comparison of the computational effort required by the present solution strategy and that required by a Dynamic Programming based approach was conducted. Subsequently, the topic of parameter uncertainty was investigated. Specifically, uncertainty in the reaction rate coefficient was considered in the analysis of the TE problem. Accordingly, the optimization problem was expanded to account for a set of different values of the reaction rate constant. Due to the complexity associated with the second scenario, the effect of uncertainty in the reaction constant was only studied for the first scenario corresponding to the optimization of the reactor section. The results obtained from this research project show that Dynamic Programming requires a CPU time that is almost two orders of magnitude larger than that required by the methodology proposed here. Likewise, the consideration of uncertainty in a physical parameter within the analysis, such as the reaction rate constant in the Tennessee Eastman problem, was shown to dramatically increase the computational load when compared to the case in which there is no process parametric uncertainty in the analysis. In general, the integration of design and control within the analysis resulted in a plant that is more economically attractive than that specified by solely optimizing the controllers but leaving the design of the different units fixed. This result is particularly relevant for this research work since it justifies the need for conducting simultaneous process design and control of chemical processes. Although the application of the robust tools resulted in conservative designs, the method has been shown to be an efficient computational tool for simultaneous design and control of chemical plants.

The process industries are characterized by the significant consumption of fresh resources. This is a critical issue, which calls for an effective strategy towards more sustainable operations. One approach that favors sustainability and resource conservation is material recycle and/or reuse. In this regard, an integrated framework is an essential element in sustainable development. An effective reuse strategy must consider the process as a whole and develop plant-wide strategies. While the role of mass and energy integration has been acknowledged as a holistic basis for sustainable design, it is worth noting that there are many design problems that are driven by properties or functionalities of the streams and not by their chemical constituency. In this dissertation, the notion of componentless design, which was introduced by Shelley and El-Halwagi in 2000, was employed to identify optimal strategies for resource conservation, material substitution, and overall process integration. First, the focus was given on the problem of identifying rigorous targets for material reuse in property-based applications by introducing a new property-based pinch analysis and visualization technique. Next, a non-iterative, property-based algebraic technique, which aims at determining rigorous targets of the process performance in materialrecycle networks, was developed. Further, a new property-based procedure for determining optimal process modifications on a property cluster diagram to optimize the allocation of process resources and minimize waste discharge was also discussed. In addition, material substitution strategies were considered for optimizing both the process and the fresh properties. In this direction, a new process design and molecular synthesis methodology was evolved by using the componentless property-cluster domain and Group Contribution Methods (GCM) as key tools in developing a generic framework and systematic approach to the problem of simultaneous process and molecular design.

No description available.